Drone Delivery: Urban airspace traffic density estimation

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

480 Downloads (Pure)


The concept of autonomous drone delivery in urban areas has gained a favorable amount of media attention over the past few years. Companies such as Amazon, Uber and Matternet are investigating the use of drones to transport parcels in order to solve the disaggregate delivery (last-mile) problem. This solution could potentially reduce vehicular congestion in cities by replacing traditional transport modes used in last-mile delivery, such as trucks, vans and bikes, with a fleet of autonomous drones flying in an urban airspace. To realize this concept, the design of an urban airspace for drones is necessary. However, the design of an urban airspace for drones will depend on critical design metrics such as drone traffic densities, traffic distribution patterns, distance between origin-destination, and the number of distribution centers. For this study, we first tackle the first metric, drone traffic density. This metric will provide an indication for the required urban airspace capacity and its expected demand. This paper therefore establishes a framework for determining the traffic density of delivery drones for a typical urban city airspace in Europe. In addition, the paper presents a cost-analysis study for fast-food delivery via drones relative to electric bikes.
Original languageEnglish
Title of host publication8th SESAR Innovation Days, 2018
Number of pages8
Publication statusPublished - 2018
EventSIDs2018: 8th SESAR Innovation Days - Salzburg, Austria
Duration: 3 Dec 20187 Dec 2018
Conference number: 8


ConferenceSIDs2018: 8th SESAR Innovation Days
Abbreviated titleSIDs2018


  • Drone
  • U-Space
  • UTM
  • delivery
  • Parcel demand
  • Traffic density
  • forecast,
  • Urban airspace
  • Food-delivery


Dive into the research topics of 'Drone Delivery: Urban airspace traffic density estimation'. Together they form a unique fingerprint.

Cite this